Superficial radially resolved fluorescence and 3D photochemical time-dependent model for photodynamic therapy

Digital Histopathological Discrimination of Label-Free Tumoral Tissues by Artificial Intelligence Phase-Imaging Microscopy

Histopathology is the gold standard for disease diagnosis. The use of digital histology on fresh samples can reduce processing time and potential image artifacts, as label-free samples do not need to be fixed nor stained. This fact allows for a faster diagnosis, increasing the speed of the process and the impact on patient prognosis. This work proposes, implements, and validates a novel digital diagnosis procedure of fresh label-free histological samples. The procedure is based on advanced phase-imaging microscopy parameters and artificial intelligence. Fresh human histological samples of healthy and tumoral liver, kidney, ganglion, testicle and brain were collected and imaged with phase-imaging microscopy. Advanced phase parameters were calculated from the images. The statistical significance of each parameter for each tissue type was evaluated at different magnifications of 10×, 20× and 40×. Several classification algorithms based on artificial intelligence were applied and evaluated. Artificial Neural Network and Decision Tree approaches provided the best general sensibility and specificity results, with values over 90% for the majority of biological tissues at some magnifications. These results show the potential to provide a label-free automatic significant diagnosis of fresh histological samples with advanced parameters of phase-imaging microscopy. This approach can complement the present clinical procedures.

Application of Classification Algorithms to Diffuse Reflectance Spectroscopy Measurements for Ex Vivo Characterization of Biological Tissues

Biological tissue identification in real clinical scenarios is a relevant and unsolved medical problem, particularly in the operating room. Although it could be thought that healthy tissue identification is an immediate task, in practice there are several clinical situations that greatly impede this process. For instance, it could be challenging in open surgery in complex areas, such as the neck, where different structures are quite close together, with bleeding and other artifacts affecting visual inspection. Solving this issue requires, on one hand, a high contrast noninvasive technique and, on the other hand, powerful classification algorithms. Regarding the technique, optical diffuse reflectance spectroscopy has demonstrated such capabilities in the discrimination of tumoral and healthy biological tissues. The complex signals obtained, in the form of spectra, need to be adequately computed in order to extract relevant information for discrimination. As usual, accurate discrimination relies on massive measurements, some of which serve as training sets for the classification algorithms. In this work, diffuse reflectance spectroscopy is proposed, implemented, and tested as a potential technique for healthy tissue discrimination. A specific setup is built and spectral measurements on several ex vivo porcine tissues are obtained. The massive data obtained are then analyzed for classification purposes. First of all, considerations about normalization, detrending and noise are taken into account. Dimensionality reduction and tendencies extraction are also considered. Featured spectral characteristics, principal component or linear discrimination analysis are applied, as long as classification approaches based on k-nearest neighbors (k-NN), quadratic discrimination analysis (QDA) or Naïve Bayes (NB). Relevant parameters about classification accuracy are obtained and compared, including ANOVA tests. The results show promising values of specificity and sensitivity of the technique for some classification algorithms, even over 95%, which could be relevant for clinical applications in the operating room.

Optimizing interstitial photodynamic therapy with custom cylindrical diffusers

Interstitial photodynamic therapy (iPDT) has shown promise recently as a minimally invasive cancer treatment, partially due to the development of non-toxic photosensitizers in the absence of activation light. However, a major challenge in iPDT is the pre-treatment planning process that specifies the number of diffusers needed, along with their positions and allocated powers, to confine the light distribution to the target volume as much as possible. In this work, a new power allocation algorithm for cylindrical light diffusers including those that can produce customized longitudinal (tailored) emission profiles is introduced. The proposed formulation is convex to guarantee the minimum over-dose possible on the surrounding organs-at-risk. The impact of varying the diffuser lengths and penetration angles on the quality of the plan is evaluated. The results of this study are demonstrated for different photosensitizers activated at different wavelengths and simulated on virtual tumors modeling virtual glioblastoma multiforme cases. Results show that manufacturable cylindrical diffusers with tailored emission profiles can significantly outperform those with conventional flat profiles with an average damage reduction on white matter of 15% to 55% and on gray matter of 23% to 58%.

Visualizing Photodynamic Therapy in Transgenic Zebrafish Using Organic Nanoparticles with Aggregation-Induced Emission

Photodynamic therapy (PDT) employs accumulation of photosensitizers (PSs) in malignant tumor tissue followed by the light-induced generation of cytotoxic reactive oxygen species to kill the tumor cells. The success of PDT depends on optimal PS dosage that is matched with the ideal power of light. This in turn depends on PS accumulation in target tissue and light administration time and period. As theranostic nanomedicine is driven by multifunctional therapeutics that aim to achieve targeted tissue delivery and image-guided therapy, fluorescent PS nanoparticle (NP) accumulation in target tissues can be ascertained through fluorescence imaging to optimize the light dose and administration parameters. In this regard, zebrafish larvae provide a unique transparent in vivo platform to monitor fluorescent PS bio-distribution and their therapeutic efficiency. Using fluorescent PS NPs with unique aggregation-induced emission characteristics, we demonstrate for the first time the real-time visualization of polymeric NP accumulation in tumor tissue and, more importantly, the best time to conduct PDT using transgenic zebrafish larvae with inducible liver hyperplasia as an example.

Intra-class variability in diffuse reflectance spectroscopy: application to porcine adipose tissue

Optical diffuse reflectance spectroscopy (DRS) has great potential in the study, diagnosis, and discrimination of biological tissues. Discrimination is based on massive measurements that conform training sets. These sets are then used to classify tissues according to the biomedical application. Classification accuracy depends strongly on the training dataset, which typically comes from different samples of the same class, and from different points of the same sample. The variability of these measurements is not usually considered and is assumed to be purely random, although it could greatly influence the results. In this work, spectral variations within and between samples of different animals of ex-vivo porcine adipose tissue are evaluated. Algorithms for normalization, dimensionality reduction by principal component analysis, and variability control are applied. The PC analysis shows the dataset variability, even when a variability removal algorithm is applied. The projected data appear grouped by animal in the PC space. Mahalanobis distance is calculated for every group, and an ANOVA test is performed in order to estimate the variability. The results confirm that the variability is not random and is dependent at least on the anatomical location and the specific animal. The variability magnitude is significant, particularly if the classification accuracy is needed to be high. As a consequence, it should be taken generally into account in classification problems.

Physiological considerations acting on triplet oxygen for explicit dosimetry in photodynamic therapy

The aims of this study were to determine the spatial and temporal theoretical distribution of the concentrations of Protoporphyrin IX, ³O₂ and doses of ¹O₂. The type II mechanism and explicit dosimetry in photodynamic therapy were used. Furthermore, the mechanism of respiration and cellular metabolism acting on ³O₂ were taken into account. The dermis was considered as an absorbing and a scattering medium. An analytical solution was used for light diffusion in the skin. The photophysical, photochemical and biological effects caused by PDT with the initial irradiances of 20, 60 and 150mW/cm² were studied for a time of exposure of 20min and a maximum depth of 0.5cm. We found that the initial irradiance triples its value in 0.02cm and that almost 100% of PpIX is part of the dynamics of reactions in photodynamic therapy. Additionally, with about 40μMof ³O₂ there is a balance between the consumed and supplied oxygen. Finally, we determined that with 60mW/cm², the highest dose of ¹O₂ is obtained.

Analysis of Low Intensity Laser Therapy as adjuvant to Photodynamic Therapy in Nonmelanoma Skin Cancer

The combination of Low Intensity Laser Therapy and Photodynamic Therapy constitutes a minimal invasive and highly selective alternative for the treatment of skin tumors. Photodynamic effectiveness, specially the absence of recurrence, highly depends on the type of tumor, as well as on several parameters such as those related with the optical source. Therefore it is important to have predictive tools to provide an appropriate treatment planning. This work proposes the use of a complex predictive model that includes a three-dimensional meshing of the considered tumor geometry and the optical propagation therein by a three-dimensional Monte Carlo method. The proposed tool is applied to three different types of skin tumors, squamous cell carcinoma, nodular and infiltrative basal cell carcinomas. The estimated volume of treatment, depending on the tumor type and source parameters, allows an appropriate treatment planning in order to avoid undesirable tumor recurrence.

Quantitative monitoring of the level of singlet oxygen using luminescence spectra of phosphorescent photosensitizer

The lack of accurate and robust photodynamic therapy dosimetry is one of the obstacles for the widespread clinical applications. In this study, we propose a methodology to monitor the production of reactive oxygen species in real-time using the phosphorescent spectra of metalloporphyrin based photosensitizer. The correlation among the phosphorescence intensity, the (1)O(2) quantum yield (Φ(Δ)) and the oxygen concentration [O(2)] was established. A method of determining Φ(Δ) with different [O(2)] was studied based on comparative spectrophotometry, and the quantum yield Φ(Δ) of gadolinium metalated hematoporphyrin mono ether (Gd-HMME) in methanol was determined for different [O(2)]. With our method, both [O(2)] and Φ(Δ) could be monitored simultaneously using the phosphorescence spectra. The photochemical reactions in a liquid phantom composed of Gd-HMME and (1)O(2) capture 1,3-diphenylisobenzofuran (DPBF) were correlated using the kinetics equations of singlet oxygen generation and reaction. Using our method, the (1)O(2) quantum yield becomes observable and the (1)O(2) dose rate could be calculated by the product of photosensitizer absorption and its (1)O(2) quantum yield. Moreover, this (1)O(2) dosimetry could be observed by spectral imaging intuitively without complex analysis, and is especially suitable for precise customized photodynamic treatment.